attilapiros commented on a change in pull request #29014: URL: https://github.com/apache/spark/pull/29014#discussion_r459953031
########## File path: core/src/test/scala/org/apache/spark/deploy/DecommissionWorkerSuite.scala ########## @@ -0,0 +1,401 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.deploy + +import java.util.concurrent.{ConcurrentHashMap, ConcurrentLinkedQueue} +import java.util.concurrent.atomic.AtomicBoolean + +import scala.collection.JavaConverters._ +import scala.collection.mutable +import scala.collection.mutable.ArrayBuffer +import scala.concurrent.duration._ + +import org.scalatest.BeforeAndAfterEach +import org.scalatest.concurrent.Eventually._ +import org.scalatest.time.Span + +import org.apache.spark._ +import org.apache.spark.deploy.DeployMessages.{MasterStateResponse, RequestMasterState, WorkerDecommission} +import org.apache.spark.deploy.master.{ApplicationInfo, Master, WorkerInfo} +import org.apache.spark.deploy.worker.Worker +import org.apache.spark.internal.{config, Logging} +import org.apache.spark.network.TransportContext +import org.apache.spark.network.netty.SparkTransportConf +import org.apache.spark.network.shuffle.ExternalBlockHandler +import org.apache.spark.rpc.{RpcAddress, RpcEnv} +import org.apache.spark.scheduler.{SparkListener, SparkListenerJobEnd, SparkListenerStageSubmitted, SparkListenerTaskEnd, SparkListenerTaskStart, TaskInfo} +import org.apache.spark.shuffle.FetchFailedException +import org.apache.spark.storage.BlockManagerId +import org.apache.spark.util.Utils + +class DecommissionWorkerSuite + extends SparkFunSuite + with Logging + with LocalSparkContext + with BeforeAndAfterEach { + + private val conf = new SparkConf() + private val securityManager = new SecurityManager(conf) + + private var masterRpcEnv: RpcEnv = null + private var master: Master = null + private val workerIdToRpcEnvs: mutable.HashMap[String, RpcEnv] = mutable.HashMap.empty + private val workers: mutable.ArrayBuffer[Worker] = mutable.ArrayBuffer.empty + + override def beforeEach(): Unit = { + super.beforeEach() + masterRpcEnv = RpcEnv.create(Master.SYSTEM_NAME, "localhost", 0, conf, securityManager) + master = makeMaster() + } + + override def afterEach(): Unit = { + try { + masterRpcEnv.shutdown() + workerIdToRpcEnvs.values.foreach(_.shutdown()) + workerIdToRpcEnvs.clear() + master.stop() + workers.foreach(_.stop()) + workers.clear() + masterRpcEnv = null + } finally { + super.afterEach() + } + } + + test("decommission workers should not result in job failure") { + val maxTaskFailures = conf.get(config.TASK_MAX_FAILURES) + val numTimesToKillWorkers = maxTaskFailures + 1 + val numWorkers = numTimesToKillWorkers + 1 + makeWorkers(numWorkers) + + // Here we will have a single task job and we will keep decommissioning (and killing) the + // worker running that task K times. Where K is more than the maxTaskFailures. Since the worker + // is notified of the decommissioning, the task failures can be ignored and not fail + // the job. + + sc = createSparkContext(appConf) + val executorIdToWorkerInfo = getExecutorToWorkerAssignments + val taskIdsKilled = new ConcurrentHashMap[Long, Boolean] + val listener = new RootStageAwareListener { + override def handleRootTaskStart(taskStart: SparkListenerTaskStart): Unit = { + val taskInfo = taskStart.taskInfo + assert(taskInfo.index == 0, s"Unknown task index ${taskInfo.index}") + if (taskIdsKilled.size() < numTimesToKillWorkers) { + val workerInfo = executorIdToWorkerInfo(taskInfo.executorId) + decommissionWorkerOnMaster(workerInfo, "partition 0 must die") + killWorkerAfterTimeout(workerInfo, 1) + taskIdsKilled.put(taskInfo.taskId, true) + } + } + + override def handleRootTaskEnd(taskEnd: SparkListenerTaskEnd): Unit = { + val taskInfo = taskEnd.taskInfo + assert(taskInfo.index === 0, s"Unknown task index ${taskInfo.index}") + // If a task has been killed then it shouldn't be successful + assert(taskInfo.successful === !taskIdsKilled.getOrDefault(taskInfo.taskId, false)) + } + } + sc.addSparkListener(listener) + // single task job + val jobResult = sc.parallelize(1 to 1, 1).map(_ => { + Thread.sleep(5 * 1000L); 1 + }).count() + assert(jobResult === 1) + } + + test("decommission workers ensure that shuffle output is regenerated even with shuffle service") { + val conf = appConf + conf.set(config.Tests.TEST_NO_STAGE_RETRY, true) + conf.set(config.SHUFFLE_MANAGER, "sort") + conf.set(config.SHUFFLE_SERVICE_ENABLED, true) + makeWorkers(2) + val ss = new ExternalShuffleServiceHolder(conf) + sc = createSparkContext(conf) + + // Here we will create a 2 stage job: The first stage will have two tasks and the second stage + // will have one task. The two tasks in the first stage will be long and short. We decommission + // and kill the worker after the short task is done. Eventually the driver should get the + // executor lost signal for the short task executor. This should trigger regenerating + // the shuffle output since we cleanly decommissioned the executor, despite running with an + // external shuffle service. + try { + val executorIdToWorkerInfo = getExecutorToWorkerAssignments + val task0Killed = new AtomicBoolean(false) + // single task job + val listener = new RootStageAwareListener { + override def handleRootTaskEnd(taskEnd: SparkListenerTaskEnd): Unit = { + val taskInfo = taskEnd.taskInfo + assert(taskInfo.index <= 1, s"Unknown task index ${taskInfo.index}") + if (taskInfo.index == 0) { + if (task0Killed.compareAndSet(false, true)) { + assert(taskInfo.successful) + // Since this task hasn't been killed before, it should still be at its first attempt. + assert(taskInfo.attemptNumber === 0) + val workerInfo = executorIdToWorkerInfo(taskInfo.executorId) + decommissionWorkerOnMaster(workerInfo, "Kill early done map worker") + killWorkerAfterTimeout(workerInfo, 0) + logInfo(s"Killed the node ${workerInfo.hostPort} that was running the early task") + } else { + assert(taskInfo.successful) + // The first attempt of this task should have failed since it was killed. Thus, + // either the task attempt or the stage attempt number should be more than 0. + assert(taskInfo.attemptNumber > 0 || taskEnd.stageAttemptId > 0) + } + } else { + // The second task is never touched and thus should succeed on the first attempt. + assert(taskInfo.successful) + assert(taskInfo.attemptNumber === 0) + } + } + } + sc.addSparkListener(listener) + val jobResult = sc.parallelize(1 to 2, 2).mapPartitionsWithIndex((pid, iter) => { + val sleepTimeSeconds = if (pid == 0) 1 else 10 + Thread.sleep(sleepTimeSeconds * 1000L) + List.fill(pid + 1)(pid * 2 + 1).iterator + }, preservesPartitioning = true).repartition(1).sum() + assert(jobResult > 1) + // 4 tasks: 2 from first stage, one retry due to decom, one more in the second stage. + val tasksSeen = listener.getTasksFinished(10.seconds) + assert(tasksSeen.size >= 4, s"Expected at least 4 tasks but got $tasksSeen") + } finally { + ss.close() + } + } + + test("decommission workers ensure that fetch failures lead to rerun") { + val conf = appConf + conf.set(config.Tests.TEST_NO_STAGE_RETRY, false) + conf.set(config.UNREGISTER_OUTPUT_ON_HOST_ON_FETCH_FAILURE, true) + makeWorkers(2) + sc = createSparkContext(conf) + val executorIdToWorkerInfo = getExecutorToWorkerAssignments + val executorToDecom = executorIdToWorkerInfo.keysIterator.next + val workerToDecomInfo = executorIdToWorkerInfo(executorToDecom) + val workerToDecomHost = workerToDecomInfo.host + val workerToDecomPort = workerToDecomInfo.port + // The setup of this job is similar to the one above: 2 stage job with first stage having + // long and short tasks. Except that we want the shuffle output to be regenerated on a + // fetch failure instead of an executor lost. Since it is hard to "trigger a fetch failure", + // we manually raise the FetchFailed exception when the 2nd stage's task runs and require that + // fetch failure to trigger a recomputation. + logInfo(s"Will try to decom the task running on executor $executorToDecom") + val listener = new RootStageAwareListener { + override def handleRootTaskEnd(taskEnd: SparkListenerTaskEnd): Unit = { + val taskInfo = taskEnd.taskInfo + if (taskInfo.executorId == executorToDecom && taskInfo.attemptNumber == 0 && + taskEnd.stageAttemptId == 0) { + decommissionWorkerOnMaster(workerToDecomInfo, + "decommission worker after task on it is done") + } + } + } + sc.addSparkListener(listener) + val jobResult = sc.parallelize(1 to 2, 2).mapPartitionsWithIndex((index, _) => { + val executorId = SparkEnv.get.executorId + val sleepTimeSeconds = if (executorId == executorToDecom) 10 else 1 + Thread.sleep(sleepTimeSeconds * 1000L) + List.fill(index + 1)(index * 2 + 1).iterator + }, preservesPartitioning = true) + .repartition(1).mapPartitions(iter => { + val context = TaskContext.get() + if (context.attemptNumber == 0 && context.stageAttemptNumber() == 0) { + // MapIndex is explicitly -1 to force the entire host to be decommissioned + // However, this will cause both the tasks in the preceding stage since the host here is + // "localhost" (shortcoming of this single-machine unit test in that all the workers + // are actually on the same host) + throw new FetchFailedException(BlockManagerId(executorToDecom, + workerToDecomHost, workerToDecomPort), 0, 0, -1, 0, "Forcing fetch failure") + } + val sumVal: List[Int] = List(iter.sum) + sumVal.iterator + }, preservesPartitioning = true) + .sum() + assert(jobResult > 1) + // 6 tasks: 2 from first stage, 2 rerun again from first stage, 2nd stage attempt 1 and 2. + val tasksSeen = listener.getTasksFinished(10.seconds) + assert(tasksSeen.size === 6, s"Expected 6 tasks but got $tasksSeen") + } + + private abstract class RootStageAwareListener extends SparkListener { + protected var rootStageId: Option[Int] = None + var tasksFinished = new ConcurrentLinkedQueue[String]() + var jobDone = new AtomicBoolean(false) + + protected def isRootStageId(stageId: Int): Boolean = + (rootStageId.isDefined && rootStageId.get == stageId) + + override def onStageSubmitted(stageSubmitted: SparkListenerStageSubmitted): Unit = { + if (stageSubmitted.stageInfo.parentIds.isEmpty && rootStageId.isEmpty) { + rootStageId = Some(stageSubmitted.stageInfo.stageId) + } + } + + override def onJobEnd(jobEnd: SparkListenerJobEnd): Unit = { + jobDone.set(true) + } + + protected def handleRootTaskEnd(end: SparkListenerTaskEnd) = {} + + protected def handleRootTaskStart(start: SparkListenerTaskStart) = {} + + override def onTaskStart(taskStart: SparkListenerTaskStart): Unit = { + if (isRootStageId(taskStart.stageId)) { + handleRootTaskStart(taskStart) + } + } + + override def onTaskEnd(taskEnd: SparkListenerTaskEnd): Unit = { + val taskSignature = s"${taskEnd.stageId}:${taskEnd.stageAttemptId}:" + + s"${taskEnd.taskInfo.index}:${taskEnd.taskInfo.attemptNumber}" + logInfo(s"Task End $taskSignature") + tasksFinished.add(taskSignature) + if (isRootStageId(taskEnd.stageId)) { + handleRootTaskEnd(taskEnd) + } + } + + def getTasksFinished(waitTime: Span): Seq[String] = eventually(timeout(waitTime)) { + val done = jobDone.get() + assert(done) + val tasksFinishedCopy = new ArrayBuffer[String]() + tasksFinishedCopy.appendAll(tasksFinished.asScala) + tasksFinishedCopy + } + } + + private def getExecutorToWorkerAssignments: Map[String, WorkerInfo] = { + val executorIdToWorkerInfo = mutable.HashMap[String, WorkerInfo]() + master.workers.foreach(wi => { + assert(wi.executors.size <= 1, "There should be at most one executor per worker") + // Cast the executorId to string since the TaskInfo.executorId is a string + wi.executors.values.foreach(e => { + val executorIdString = e.id.toString + val oldWorkerInfo = executorIdToWorkerInfo.put(executorIdString, wi) + assert(oldWorkerInfo.isEmpty, + s"Executor $executorIdString already present on another worker ${oldWorkerInfo}") + }) + }) + executorIdToWorkerInfo.toMap + } + + private def appConf: SparkConf = { + new SparkConf() + .setMaster(masterRpcEnv.address.toSparkURL) + .setAppName("test") + .set(config.EXECUTOR_CORES.key, "1") + .set(config.EXECUTOR_MEMORY.key, "2048m") // one exec per worker + } + + private def makeMaster(): Master = { + val master = new Master(masterRpcEnv, masterRpcEnv.address, 0, securityManager, conf) + masterRpcEnv.setupEndpoint(Master.ENDPOINT_NAME, master) + master + } + + private def makeWorkers(numWorkers: Int, cores: Int = 1, memory: Int = 2048): Unit = { + val workerRpcEnvs = (0 until numWorkers).map { i => + RpcEnv.create(Worker.SYSTEM_NAME + i, "localhost", 0, conf, securityManager) + } + workers.clear() + val rpcAddressToRpcEnv: mutable.HashMap[RpcAddress, RpcEnv] = mutable.HashMap.empty + workerRpcEnvs.foreach { rpcEnv => + val worker = new Worker(rpcEnv, 0, cores, memory, Array(masterRpcEnv.address), + Worker.ENDPOINT_NAME, null, conf, securityManager) + rpcEnv.setupEndpoint(Worker.ENDPOINT_NAME, worker) + workers.append(worker) + val oldRpcEnv = rpcAddressToRpcEnv.put(rpcEnv.address, rpcEnv) + assert(oldRpcEnv.isEmpty, s"Detected duplicate rpcEnv ${oldRpcEnv} for ${rpcEnv.address}") + } + workerIdToRpcEnvs.clear() + // Wait until all workers register with master successfully + eventually(timeout(1.minute), interval(1.seconds)) { + val workersOnMaster = getMasterState.workers + val numWorkersCurrently = workersOnMaster.size + logInfo(s"Waiting for $numWorkers workers to come up: So far $numWorkersCurrently") + assert(numWorkersCurrently === numWorkers) + workersOnMaster.foreach {workerInfo => + val rpcAddress = RpcAddress(workerInfo.host, workerInfo.port) + val rpcEnv = rpcAddressToRpcEnv(rpcAddress) + assert(rpcEnv != null, s"Cannot find the worker for $rpcAddress") + val oldRpcEnv = workerIdToRpcEnvs.put(workerInfo.id, rpcEnv) + assert(oldRpcEnv.isEmpty, s"Detected duplicate rpcEnv ${oldRpcEnv} for worker " + + s"${workerInfo.id}") + } + } + logInfo(s"Created ${workers.size} workers") + } + + private def getMasterState: MasterStateResponse = { + master.self.askSync[MasterStateResponse](RequestMasterState) + } + + private def getApplications(): Seq[ApplicationInfo] = { + getMasterState.activeApps + } + + def decommissionWorkerOnMaster(workerInfo: WorkerInfo, reason: String): Unit = { + logInfo(s"Trying to decom worker ${workerInfo.id} on ${workerInfo.hostPort} for reason $reason") Review comment: The `on ${workerInfo.hostPort}` can be removed as it its value is not so interesting for the test, moreover the worker name already contains this info too, see: ``` 20/07/24 02:05:16.097 spark-listener-group-shared INFO DecommissionWorkerSuite: Trying to decom worker worker-20200724020501-localhost-54753 on localhost:54753 for reason decommission worker after task on it is done ``` ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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